docs: def from_characteristic_rate(cls, min_mag, b_val, char_mag, char_rate,
                                   bin_width):
          """
          Define Youngs and Coppersmith 1985 MFD by constraing cumulative a
          value from characteristic rate.
          The cumulative a value is obtained by making use of the property that
          the rate of events at m' - 1 must be equal to the rate at the
          characteristic magnitude, and therefore by first computing the
          incremental a value, using the following equation::
  
              10 ** (a_incr - b_val * (m_prime - 1)) == char_rate / 0.5
  
          where ``m' - 1 = char_mag - 1.25``.
          The cumulative a value is then obtained as ::
  
              a_val = a_incr - log10(b_val * ln(10))
  
          :param min_mag:
              The lowest magnitude for the MFD. The first bin in the
              :meth:`result histogram <get_annual_occurrence_rates>` is aligned
              to make its left border match this value.
          :param b_val:
              The Gutenberg-Richter ``b`` value -- the gradient of the loglinear
              G-R relationship.
          :param char_mag:
              The characteristic magnitude defining the middle point of
              characteristic distribution. That is the boxcar function
              representing the characteristic distribution is defined in the
              range [char_mag - 0.25, char_mag + 0.25].
          :param char_rate:
              The characteristic rate associated to the characteristic magnitude,
              to be distributed over the domain of the boxcar function
              representing the characteristic distribution (that is λ_char =
              char_rate / 0.5)
          :param bin_width:
              A positive float value -- the width of a single histogram bin.
          :returns:
              An instance of :class:`YoungsCoppersmith1985MFD`.
  
          Values for ``min_mag`` and the maximum magnitude (char_mag + 0.25)
          don't have to be aligned with respect to ``bin_width``. They get
          rounded accordingly anyway so that both are divisible by ``bin_width``
          just before converting a function to a histogram.
          See :meth:`_get_min_mag_and_num_bins`.
          """
          a_incr = b_val * (char_mag - 1.25) + numpy.log10(char_rate /
                                                           DELTA_CHAR)
          a_val = a_incr - numpy.log10(b_val * numpy.log(10))
  
          return cls(min_mag, a_val, b_val, char_mag, char_rate, bin_width)